⚡ Bolt: [performance improvement] Optimize iterrows in ETL pipeline

[Vagarh]

💡 What: Replaced Pandas DataFrame conversion and df.iterrows() with a direct loop over a list of dictionaries (data) inside the load_data function in public_data_etl.py.

🎯 Why: Converting a perfectly good native list of dictionaries into a Pandas DataFrame solely to iterate over its rows using iterrows() is a massive anti-pattern in Airflow pipelines. iterrows() creates a Pandas Series for every single row, causing huge memory and O(n) CPU overhead, while a simple python for loop is lightning-fast and requires no extra memory.

📊 Impact: Massively reduces CPU and memory utilization during the load_data DAG step, which is particularly beneficial as the dataset size grows or Airflow worker memory is constrained.

🔬 Measurement: Local profiling would show almost zero memory allocation during the tuple preparation block and vastly faster completion times compared to iterating via DataFrames.

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PR created automatically by Jules for task 11047276891903090654 started by @Vagarh

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